Why does SVM-linear kernel outperforms SVM-RBF kernel in terms of classification accuracy?

Question

I am trying to classify the spectroscopy data, the SVM-linear is performing better than SVM-RBF in terms of classification accuracy, I was expecting the other way. Why is it so? Any reason? How can I justify this?

Answer 1

If your data is linearly separable (linear frontier between the two classes hyper-plane's regions) it is to be expected that the linear kernel would have the best performance. If you miss parametrised your RBF kernel this could also be the case, try different values for $gamma$ and the Cost parameters.

There is no reason to say apriori that the RBF kernel is best, it really depends on your data. It just happens that in most problems the RBF kernel performs best and so people tend to go for it more often than not.